Many solutions to anti-spoofing for automatic speaker verification have focused upon the design of deep neural network (DNN) based back-end classifiers. Most combine fixed, hand-crafted features, usually in the form of some spectro-temporal decomposition [1]}, [2]}, with a convolutional neural network (CNN) to learn higher-level representations. The literature shows that the use of specially designed network modules [3]}, [4]}, [5]} and loss functions [6]}, [7]}, [8]} generally leads to better performing models. Still, their potential is fundamentally dependent upon the information captured in the initial features; information lost in initial feature extraction cannot be recovered. Several works have also shown that the performance of a given model can vary substantially when fed with different features [3]}, [4]}, [6]}. These observations point toward the importance of learning and optimising not just the higher-level representation, but also the initial features, in unison with the classifier.
